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Spatiotemporal Evolution of Ridesourcing Markets Under the New Restriction Policy: A Case Study in Shanghai

In: Logic-Driven Traffic Big Data Analytics

Author

Listed:
  • Shaopeng Zhong

    (Dalian University of Technology
    Southwest Jiaotong University)

  • Daniel (Jian) Sun

    (Chang’an University
    Shanghai Jiao Tong University)

Abstract

Identifying and understanding factors that influence the demand of ridesourcing market is essential for online hailing systems to improve the quality of service. This chapter proposes a two-level growth model (GM) to identify the potential multi-level factors that may affect online ride-hailing service demand. By using the massive datasets from Didi Chuxing, Inc., including both Didi Express and Didi Taxi services, the order number fluctuations at different urban circle zones after the implementation of restrictions on ridesourcing in Shanghai, 2016 were analyzed, to assess the competition and mutual complementarities between Express and Taxi, the two major services provided by Didi Chuxing. The relative market share of Express was estimated to reveal the possible related spatial and temporal factors, which further demonstrates significant positive associations between ridesourcing demand and built environment factors, such as commercial/residential land use, public transport accessibility, as well as weather conditions. Metro service availability and rainy weather were found correlated with a relatively higher market share of Express service. Additionally, compared to the regular road transit service, the metro system was found to have a stronger correlation with the ridesourcing demand. Findings of this study may provide guidelines for urban planning and traffic operations, which in turn assists to achieve high-quality ridesourcing service for travellers.

Suggested Citation

  • Shaopeng Zhong & Daniel (Jian) Sun, 2022. "Spatiotemporal Evolution of Ridesourcing Markets Under the New Restriction Policy: A Case Study in Shanghai," Springer Books, in: Logic-Driven Traffic Big Data Analytics, chapter 0, pages 53-72, Springer.
    • Handle: RePEc:spr:sprchp:978-981-16-8016-8_3
    DOI: 10.1007/978-981-16-8016-8_3
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    Cited by:

    1. Zilong Zhao & Daxin Tian & Xuting Duan & Randong Xiao, 2023. "Joint Optimization of Battery Swapping Scheduling for Electric Taxis," Sustainability, MDPI, vol. 15(18), pages 1-11, September.

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